739 research outputs found
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
Development of computational tools for modeling engine fuel economy and emissions
The Integrated Bus Information System (IBIS) is a vehicle fleet emission and fuel economy prediction software. IBIS is under development by faculty and students of West Virginia University (WVU). The overall goal of IBIS is to provide an approachable and reliable method for users, primarily transit agencies, to evaluate overall fleet emissions and fuel consumption. This approach differs from current modeling packages as IBIS is an online tool and allows for a customizable, user-defined vehicle fleet.;The modeling strategy for IBIS involves creating models using data obtained from the WVU Center for Alternative Fuels, Engines, and Emissions (CAFEE) testing database. These models are multiple variable polynomials created through regression analysis. Additionally, multiplicative and additive correction factors are computed and applied to backbone models to account for variances in vehicle configurations and technologies.;This modeling strategy includes the necessary development of tools to aid in the creation of continuous models. The first to be implemented is a polynomial regression tool. This methodology utilizes data gleaned from the WVU Center for Alternative Fuels, Engines and Emission database. The tool is designed to perform multivariable regression for standard driving cycles: where second-by-second data is available.;The accuracy of these models is reliant upon large sets of data. Furthermore, in cases where limited a dataset is available, additional information may be computed by concatenating experimental data isolated from within existing testing cycles for which testing has been preformed. This data is extracted from a driving cycle by defining periods of non-idle. These periods, or microtrips, are rearranged into new cycles of varying length by a second computational tool.;This second tool is a driving cycle generator which utilizes a genetic algorithm to reorder and concatenate microtrips such that the resulting cycle fulfills criteria supplied by the user. These parameters align with input parameters defining a driving cycle for both IBIS and the polynomial tool: parameters include average speed with idle, standard deviation of speed with idle, kinetic intensity, percentage idle, and number of stops per mile. In addition to providing additional data, the cycle generator yields insight as to acceptable limits on the user inputs defining a driving cycle.;Once the data set has been expanded by the cycle generator, the new data is reintroduced to the polynomial regression tool. Expansion of the data set allows the polynomial tool to generate a much more realistic trend for a domain of average speed than was previously obtained with limited data. With the integration of the cycle generator into the polynomial tool, adverse effects caused by interpolation are significantly minimized in the polynomial model.;The use of the polynomial tool has improved and accelerated the design process for models for IBIS. Additionally, the integration of the newly generated cycles through the use of a GA allows for accurate expansion of experimental data without necessitating supplementary dynamometer testing
IDENTIFICATION OF HEAT RELEASE SHAPES AND COMBUSTION CONTROL OF AN LTC ENGINE
Low Temperature Combustion (LTC) regimes have gained attention in internal combustion engines since they deliver low nitrogen oxides (NOx) and soot emissions with higher thermal efficiency and better combustion efficiency, compared to conventional combustion regimes. However, the operating region of these high-efficiency combustion regimes is limited as it is prone to knocking and high in-cylinder pressure rise rate outside the engine safe zone. By allowing multi-regime operation, high-efficiency region of the engine is extended. To control these complex engines, understanding and identification of heat release rate shapes is essential. Experimental data collected from a 2 liter 4 cylinder LTC engine with in-cylinder pressure measurements, is used in this study to calculate Heat Release Rate (HRR). Fractions of early and late heat release are calculated from HRR as a ratio of cumulative heat release in the early or late window to the total energy of the fuel injected into the cylinder. Three specific HRR patterns and two transition zones are identified. A rule based algorithm is developed to classify these patterns as a function of fraction of early and late heat release percentages. Combustion parameters evaluated also showed evidence on characteristics of classification. Supervised and unsupervised machine learning approaches are also evaluated to classify the HRR shapes. Supervised learning method ( Decision Tree)is studied to develop an automatic classifier based on the control inputs to the engine. In addition, supervised learning method (Convolutional Neural Network (CNN)) and unsupervised learning method (k-means clustering) are studied to develop an automatic classifier based on HRR trace obtained from the engine. The unsupervised learning approach wasn\u27t successful in classification as the arrived k-means centroids didn\u27t clearly represent a particular combustion regime. Supervised learning techniques, CNN method is found with a classifier accuracy of 70% for identifying heat release shapes and Decision Tree with the accuracy of 74.5% as a function of control inputs.
On rule based classified traces with the use of principle component analysis (PCA) and linear regression, heat release rate classifiers are built as a function of engine input parameters including, Engine speed, Start of injection (SOI), Fuel quantity (FQ) and Premixed ratio (PR). The results are then used to build a linear parameter varying (LPV) model as a function of the modelled combustion classifiers by using the least square support vector machine (LS-SVM) approach. LPV model could predict CA50(Combustion phasing), IMEP (indicated mean effective pressure) and MPRR (maximum pressure rise rate) with a RMSE of 0.4 CAD, 16.6 kPa and 0.4 bar/CAD respectively. The designed LPV model is then incorporated in a model predictive control (MPC) platform to adjust CA50, IMEP and MPRR. The results show the designed LTC engine controller could track CA50 and IMEP with average error of 1.2 CAD and 6.2 kPa while limiting MPRR to 6 bar/CAD. The controller uses three engine inputs including, SOI, PR and FQ as manipulated variables, that are optimally changed to control the LTC engine
Optimal control of a motor-integrated hybrid powertrain for a two-wheeled vehicle suitable for personal transportation
The present research aims to propose an optimized configuration of the motor integrated power-train with an optimal controller suitable for small power-train based two wheeler automobile which can increase the system level efficiency without affecting drivability. This work will be the foundation for realizing the system in a production ready vehicle for the two wheeler OEM TVS Motor Company in India. A detailed power-train model is developed (from first principles) for the scooter vehicle, which is powered by a 110 cc spark ignition (SI) engine and coupled with two types of transmission, a continuous variable transmission (CVT) and a 4-speed manual transmission (MT). Both models are capable of simulating torque and NOx emission output of the SI engine and dynamic response of the full power-train. The torque production and emission outputs of the model are compared with experimental results available from TVS Motor Company. The CVT gear ratio model is developed using an indirect method and an analytical model. Both types of powertrain models are applied to perform a simulated study of fuel consumption, NOx emission and drivability study for a particular vehicle platform. In the next stage of work, the mathematical model for a brush-less direct current machine (BLDC) with the drive system and Li-Ion battery are developed. The models are verified and calibrated with the experimental results from TVS Motor Company. The BLDC machine is integrated with both the CVT and MT powertrain models in parallel hybrid configurations and a drive cycle simulation is conducted for different static assist levels by the electrical machines. The initial test confirms the need of optimal sizing of the powertrain components as well as an optimal control system. The detailed model of the powertrain is converted to a control-oriented model which is suitable for optimal control. This is followed by multi-objective optimization of different components of the motor-integrated powertrain using a single function as well as Pareto-Optimal methods. The objective function for the multi-objective optimization is proposed to reduce the fuel consumption with battery charge sustainability with least impact on the increase of financial cost and weight of the vehicle. The optimization is conducted by a nested methodology that involves Particle Swarm Optimization and a Non-dominated sorting genetic algorithm where, concurrently, a global optimal control is developed corresponding to the multi-objective design. The global optimal controller is designed using dynamic programming. The research is concluded with an optimal controller developed using the hp-collocation method. The objective function of the dynamic programming method and hp-collocation method is proposed to reduce fuel consumption with battery charge sustainability.Open Acces
Combined fault detection and classification of internal combustion engine using neural network
Different faults in internal combustion engines leads to excessive fuel consumption, pollution, acoustic emission and wear of engine components. Detection of fault is also difficult for maintenance technicians due to broad range of faults and combination of the faults. In this research the faults due to malfunction of manifold absolute pressure, knock sensor and misfire are detected and classified by analyzing vibration signals. The vibration signals acquired from engine block were preprocessed by wavelet analysis, and signal energy is considered as a distinguishing property to classify these faults by a Multi-Layer Perceptron Neural Network (MLPNN). The designed MLPNN can classify these faults with almost 100 % efficiency
Development of Automated Calibration Methodology for Last Generation of Diesel Automotive Powertrains
L'abstract è presente nell'allegato / the abstract is in the attachmen
Data driven nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions
Diesel engines' reliable and durable structures, high torque generation capabilities at low speeds, and fuel consumption efficiencies make them irreplaceable for heavy-duty vehicles in the market. However, ine ciencies in the combustion process result in the release of emissions to the environment. In addition to the restrictive international regulations for emissions, the competitive demands for more powerful engines and increasing fuel prices obligate heavy-duty engine and vehicle manufacturers to seek for solutions to reduce the emissions while meeting the performance requirements. In line with these objectives, remarkable progress has been made in modern diesel engine systems such as air handling, fuel injection, combustion, and after-treatment. However, such systems utilize quite sophisticated equipment with a large number of calibratable parameters that increases the experimentation time and effort to find the optimal operating points. Therefore, a dynamic model-based transient calibration is required for an e cient combustion optimization which obeys the emission limits, and meets the desired power and efficiency requirements. This thesis is about developing optimizationoriented high delity nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions. Contributions of the thesis are: (i) A new design of experiments is proposed where air-path and fuel-path input channels are excited by chirp signals with varying frequency pro les in terms of the number and directions of the sweeps. The proposed approach is a strong alternative to the steady-state experiment based approaches to reduce the testing time considerably and improve the modeling accuracy in both steady-state and transient conditions. (ii) A nonlinear nite impulse response (NFIR) model is developed to predict indicated torque by including the estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer. (iii) Two different nonlinear autoregressive with exogenous input (NARX) models are proposed to predict NOx emissions. In the first structure, input regressor set for the nonlinear part of the model is reduced by an orthogonal least square (OLS) algorithm to increase the robustness and decrease the sensitivity to parameter changes, and linear output feedback is employed. In the second structure, only the previous output is used as the output regressor in the model due to the stability considerations. (iv) An analysis of model sensitivities to parameter changes is conducted and an easy-tointerpret map is introduced to select the best modeling parameters with limited testing time in powertrain development. (v) Soot (particulated matter) emission is predicted using LSTM type networks which provide more accurate and smoother predictions than NARX models. Experimental results obtained from the engine dynamometer tests show the e ectiveness of the proposed models in terms of prediction accuracies in both NEDC (New European Driving Cycle) and WHTC (World Harmonized Transient Cycle) cycle
Rapid prototyping of distributed systems of electronic control units in vehicles
Existing vehicle electronics design is largely divided by feature, with integration taking place at a late stage. This leads to a number of drawbacks, including longer development time and increased cost, both of which this research overcomes by considering the system as a whole and, in particular, generating an executable model to permit testing. To generate such a model, a number of inputs needed to be made available. These include a structural description of the vehicle electronics, functional descriptions of both the electronic control units and the communications buses, the application code that implements the feature and software patterns to implement the low-level interfaces to sensors and actuators. [Continues.
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